Voyage AI Rerank 2.5
First reranker with instruction-following capabilities enabling dynamic query steering and domain-specific optimization. Supports 32K token context (8x Cohere's) through advanced distillation techniques. If you want to compare the best rerankers for your data, try Agentset.
Model Information
- Provider
- Voyage AI
- License
- Proprietary
- Price per 1M tokens
- $0.050
- Release Date
- 2025-08-11
- Model Name
- voyage-rerank-2.5
- Total Evaluations
- 3300
Performance Record
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Performance Overview
ELO ratings by dataset
Voyage AI Rerank 2.5's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Voyage AI Rerank 2.5 - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
DBPedia
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 583ms
- P50 (Median)
- 613ms
- P90
- 632ms
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 612ms
- P50 (Median)
- 521ms
- P90
- 734ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.108
- nDCG@10
- 0.119
- Recall@5
- 0.098
- Recall@10
- 0.128
Latency Distribution
- Mean
- 627ms
- P50 (Median)
- 611ms
- P90
- 814ms
arguana
Accuracy Metrics
- nDCG@5
- 0.536
- nDCG@10
- 0.543
- Recall@5
- 0.960
- Recall@10
- 0.980
Latency Distribution
- Mean
- 675ms
- P50 (Median)
- 612ms
- P90
- 820ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 571ms
- P50 (Median)
- 611ms
- P90
- 647ms
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 612ms
- P50 (Median)
- 612ms
- P90
- 791ms
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with top-ranked rerankers and embedding models built in. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Compare Models
See how it stacks up
Compare Voyage AI Rerank 2.5 with other top rerankers to understand the differences in performance, accuracy, and latency.